Grounding pipeline
Retrieval built against your actual documentation, help content, or internal wiki, kept current as that material changes.
Applied AI
Bots that answer from your data, not a generic model's guesses
A support bot, sales bot, or internal knowledge bot only earns its place if it is grounded in your actual documentation, knows when to hand off, and you can prove it reduced someone's workload.
Most bot projects fail for the same reason. Someone connects a general-purpose model to a chat widget, points it at a handful of PDFs, and calls it done. It answers confidently and incorrectly, nobody trusts it, and it gets quietly turned off within a quarter.
A bot worth deploying is grounded on the same material your best support rep or salesperson would actually reference: current documentation, pricing rules, past ticket resolutions, internal wikis. It should refuse to answer, or hand off to a person, when the grounding material does not cover the question, rather than filling the gap with something plausible sounding.
We build three kinds of these: customer-facing support bots that reduce repetitive ticket volume, sales-facing bots that qualify or answer product questions before a human gets involved, and internal knowledge bots that let staff ask a question instead of digging through a folder structure or messaging a coworker who might be out that day.
In every case the harder problem is not the chat interface. It is deciding what the bot is allowed to say, what it should never say, and where the handoff to a human happens. We spend more time on that boundary than on the conversational layer, because that boundary is what determines whether the bot is trusted or ignored.
What it is
Grounding, escalation, and proof it worked
A custom bot is only as good as the data it is grounded on and the honesty of its escalation logic.
Grounding means the bot retrieves from your actual current material rather than relying on what a general model was trained on months or years ago. We build the retrieval layer against your documentation, help center content, product catalog, or internal wiki, and keep it current as that material changes, so the bot does not confidently repeat a pricing tier or policy that was retired last quarter.
Escalation is the part most bot vendors skip. We define explicit conditions under which the bot stops answering and routes to a person: low confidence in the retrieved material, a question that touches account-specific or sensitive information, or a user who is clearly frustrated. A bot that never escalates is more dangerous than no bot at all, because it will eventually answer a question it should not have.
For support bots specifically, we design the handoff so the human agent receives the full conversation history and whatever the bot already attempted, instead of making the customer repeat themselves. That single detail is often the difference between a bot that customers tolerate and one they actively avoid.
None of this matters if you cannot tell whether the bot helped. We build in the tracking to answer that directly: how many conversations the bot fully resolved without escalation, how many escalated and why, and what topics keep coming up that the grounding material does not yet cover. That last signal is often the most valuable output of the whole project, because it tells you what documentation is actually missing.
Fit
Who this is for, and who it is not for
We would rather say no early than sell a program that cannot work.
Right fit
- You have a real volume of repetitive questions, whether from customers, prospects, or your own staff, and a documented source of truth to ground answers in.
- You want to reduce load on a support or sales team without removing the option for a person to step in.
- You have internal knowledge scattered across docs, wikis, and people's heads, and want a single place to ask instead of searching.
Not the right fit
- You want a bot to fully replace a support or sales team with no human fallback. We will not build one without an escalation path.
- You have no current documentation or source material to ground answers in. That has to exist or get built first.
- You want the bot to make final decisions on anything with legal, financial, or safety consequences without a human review step.
Deliverables
What you get
A grounded bot, a defined escalation path, and a way to measure whether either one is working.
Purpose-built bot
A support, sales, or internal knowledge bot scoped to a specific set of questions rather than a generic open-ended assistant.
Escalation logic
Explicit rules for when the bot hands off to a person, with full conversation context passed along so nobody repeats themselves.
Guardrails
Defined boundaries on what the bot will and will not answer, so it fails safely instead of guessing.
Deployment on your channel
Delivered where the conversation already happens, whether that is a help center, a website widget, or an internal tool.
Deflection and gap reporting
Tracking on resolution rate, escalation reasons, and recurring questions the grounding material does not yet cover.
How we run it
How a bot gets built
Grounding and escalation get defined before a single line of conversational design happens.
Step 1: Scope and source material audit
We identify the specific question set the bot needs to handle and audit whether the documentation to ground it on actually exists and is current.
Step 2: Grounding and escalation design
We build the retrieval layer against real source material and define, in writing, exactly when the bot should hand off instead of answering.
Step 3: Build and internal testing
The bot gets built and tested against real historical questions, including edge cases it should refuse, before anyone outside the project sees it.
Step 4: Deploy and measure
We launch with tracking already in place, then review deflection, escalation reasons, and content gaps on a set schedule rather than assuming it is working.
What makes a custom bot different from a generic chatbot?
It answers from your actual documentation and data instead of a general model's training, has explicit rules for when it hands off to a human, and comes with tracking to measure whether it actually resolved anything.
Where this connects
Where custom bots connect
A bot is one piece of a broader automation and agent strategy.
When a bot needs to take action rather than just answer questions, that logic is built as an agentic workflow so the bot can actually do something, not just respond.
For bots that need to operate with more independence across a longer process, see autonomous agents which covers a broader scope than a single conversational interface.
The manual process a bot is meant to reduce should first be mapped under automation workflows to confirm a bot is actually the right fix before building one.
If the bot needs to sit inside a broader internal tool rather than a standalone widget, that build falls under internal tools since that is a frontend engineering scope of its own.
Questions
Custom bots questions we get asked
Build a bot that is grounded, honest about its limits, and measurable
We define the escalation path before we write a single line of conversation.